Performance evaluation of multiple adaptive regression splines, teaching-learning based optimization and conventional regression techniques in predicting mechanical properties of impregnated wood

Basit Öğe Kaydı
dc.authoridKankal, Murat/0000-0003-0897-4742
dc.authoridBARDAK, selahattin/0000-0001-9724-4762
dc.authoridNACAR, Sinan/0000-0003-2497-5032
dc.contributor.authorTiryaki, Sebahattin
dc.contributor.authorTan, Hueseyin
dc.contributor.authorBardak, Selahattin
dc.contributor.authorKankal, Murat
dc.contributor.authorNacar, Sinan
dc.contributor.authorPeker, Hueseyin
dc.date.accessioned2025-03-23T19:44:44Z
dc.date.available2025-03-23T19:44:44Z
dc.date.issued2019
dc.departmentSinop Üniversitesi
dc.description.abstractUnderstanding the mechanical behaviour of impregnated wood is crucial in making a preliminary decision on the usability of such woods for structural purposes. In this paper, by considering concentration (1, 3 and 5%), pressure (1, 1.5 and 2atm.), and time (30, 60, 90 and 120min), an experimental study was performed, and the mechanical behaviour of impregnated wood was determined as a result of the experimental process. Multiple adaptive regression splines (MARS), teaching-learning based optimization (TLBO) algorithms and conventional regression analysis (CRA) were applied to different regression functions by using experimentally obtained data. The functions were checked against each other to detect the best equation for each parameter and to assess performances of MARS, TLBO and CRA methods in the prediction of mechanical properties. The experimental results showed that higher values of mechanical properties were obtained when lower concentration, pressure and time were chosen. Overall, all the functions successfully predicted the mechanical properties. However, the MARS and TLBO provided better accuracy in predicting the mechanical properties. The modeling results indicated that the MARS and TLBO are promising new methods in predicting the mechanical properties of impregnated wood. With the use of these methods, the mechanical behavior of impregnated wood could be determined with high levels of accuracy. Thus, the proposed methods may facilitate a preliminary decision concerning the usability of such woods for areas where the mechanical properties are important. Finally, the employment of MARS and TLBO algorithms by practitioners in the wood industry is encouraged and recommended for future studies.
dc.identifier.doi10.1007/s00107-019-01416-9
dc.identifier.endpage659
dc.identifier.issn0018-3768
dc.identifier.issn1436-736X
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85065386974
dc.identifier.scopusqualityQ1
dc.identifier.startpage645
dc.identifier.urihttps://doi.org/10.1007/s00107-019-01416-9
dc.identifier.urihttps://hdl.handle.net/11486/7008
dc.identifier.volume77
dc.identifier.wosWOS:000471701800014
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEuropean Journal of Wood and Wood Products
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250323
dc.subjectArtificial Neural-Network
dc.subjectBoric-Acid
dc.subjectModulus
dc.subjectBoron
dc.subjectElasticity
dc.subjectRupture
dc.subjectDesign
dc.subjectParameters
dc.subjectStrength
dc.subjectModels
dc.titlePerformance evaluation of multiple adaptive regression splines, teaching-learning based optimization and conventional regression techniques in predicting mechanical properties of impregnated wood
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu